Interrupted sampling repeater jamming (ISRJ) is an effective way of deceiving coherent radar sensors, especially for linear frequency modulated (LFM) radar. In this paper, for a simplified scenario with a single jammer, we propose a dynamic electronic counter-counter measure (ECCM) scheme based on jammer parameter estimation and transmitted signal design. Firstly, the LFM waveform is transmitted to estimate the main jamming parameters by investigating the discontinuousness of the ISRJ’s time-frequency (TF) characteristics. Then, a parameter-adjusted intra-pulse frequency coded signal, whose ISRJ signal after matched filtering only forms a single false target, is designed adaptively according to the estimated parameters, i.e., sampling interval, sampling duration and repeater times. Ultimately, for typical jamming scenes with different jamming signal ratio (JSR) and duty cycle, we propose two particular ISRJ suppression approaches. Simulation results validate the effective performance of the proposed scheme for countering the ISRJ, and the trade-off relationship between the two approaches is demonstrated.

This paper focuses on developing an anti-velocity jamming strategy that enhances the ability of a pulse-Doppler (PD) radar to detect moving targets in the presence of translational and/or micro motion velocity jamming generated by the digital radio frequency memory (DRFM) repeat jammers. The strategy adopts random pulse initial phase (RPIP) pulses as its transmitted signal and thus gets DRFM jammers not adaptable to the randomness of initial phase of the transmitted pulses in the pulse repetition interval (PRI) domain. The difference between the true target echo and the false target jamming signal at each PRI is then utilized to recognize the true and false target signals. In particular, an entropy based multi-channel processing scheme is designed to extract the information of the received signal without the assumption that true and false targets must be both included within one coherent processing interval (CPI). Information such as the component of the received signal (target echo only, jamming only or both) or the operating manner of DRFM repeat jammer can be gained (if jamming exists). Meanwhile, we solve the false target recognition problem under sparse theory frame and our previous work named the short-time sparse recovery (STSR) algorithm is introduced to recover the motion parameters of the true and/or false targets in the time-frequency domain. It should be pointed out that both the translational false target jamming and micro motion target jamming can be recognized in our strategy. The performance of the proposed strategy is compared with the correlated processing (CP) method used by most extant strategies. It is shown that the proposed strategy can successfully recognize the existence of true and/or false targets and keep its power in recovering corresponding motion parameters even when the jamming environment is strong.

Unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC), as a way of coping with delay-sensitive and computing-intensive tasks, is considered to be a key technology to solving the challenges of terrestrial MEC networks. In this work, we study the problem of collaborative service provisioning (CSP) for UAV-assisted MEC. Specifically, taking into account the task latency and other resource constraints, this paper investigates how to minimize the total energy consumption of all terrestrial user equipments, by jointly optimizing computing resource allocation, task offloading, UAV trajectory, and service placement. The CSP problem is a non-convex mixed integer nonlinear programming problem, owing to the complex coupling of mixed integral variables and non-convexity of CSP. To address the CSP problem, this paper proposes an alternating optimization-based solution with the convergence guarantee as follows. We iteratively deal with the joint service placement and task offloading subproblem, and UAV movement trajectory subproblem, by branch and bound and successive convex approximation, respectively, while the closed form of the optimal computation resource allocation can be efficiently obtained. Extensive simulations validate the effectiveness of the proposed algorithm compared to three baselines.

Device-to-device (D2D)-based offloading stands out as a promising approach to mitigate the pressure over cellular networks. From the perspectives of both operators and users, an ideal solution is expected to maximize the offloading efficiency (ratio) while guaranteeing the quality-of-service (QoS) of users. However, existing approaches seldom jointly take into consideration offloading efficiency optimization, and QoS guarantee. In this paper, we study how to maximize the percentage of total offloaded traffic of mobile content downloading via D2D, while guaranteeing the user minimum throughput under limited buffer size. The major challenge is how to incorporate all potential D2D opportunities to maximize the offloading performance. We propose a spatial-temporal dynamic graph (STDG) to capture all possible D2D opportunities in D2D-based traffic offloading. Based on STDG, we mathematically formulate an optimization problem of maximizing the offloading efficiency, subject to throughput requirement and buffer size constraint. The formulated problem is a linear-fractional problem and can be transformed into an equivalent linear program, whose solution reveals the maximum offloading ability that D2D achieves under QoS satisfaction. Trace-based simulations validate the superiority of our approach over three benchmark approaches.

Addressing the high-dimensional, strongly constrained multi-objective optimization problem of air–ground collaborative jamming scheduling in complex terrain, existing methods are often limited by incomplete modeling and low optimization efficiency in discrete feasible regions. This paper proposes a Terrain-Aware Multi-Scale Discrete Operator (TA-MSDO). A joint optimization model integrating discrete terrain characteristics and practical combat constraints is first constructed. Then, by leveraging the topological adjacency of terrain units, TA-MSDO employs a block-level crossover and a multi-scale mutation mechanism, replacing traditional continuous genetic operations to enable efficient and directional exploration of the discrete feasible region. Integrating TA-MSDO into the NSGA-III framework yields the enhanced ENSGA3 algorithm. Experimental results in a typical hilly terrain scenario demonstrate that ENSGA3 achieves a statistically significant performance improvement over the decomposition-based MOEA/D algorithm in terms of maximum achievable suppression effectiveness and hypervolume. As a comprehensive metric integrating convergence and Pareto frontier coverage, hypervolume further verifies the superior comprehensive optimization capability of the proposed algorithm. Meanwhile, compared with other classic mainstream multi-objective optimization algorithms including NSGA-II, standard NSGA-III and SPEA2, the proposed algorithm exhibits clear positive advantages in the upper bound of suppression effectiveness for elite solutions and operational stability across random initializations, with a favorable trend in Pareto frontier coverage for multi-objective collaborative optimization. This work provides an effective solution for jamming resource scheduling in complex battlefield environments.

In recent years, the rapid development of air-ground communication networks has posed challenges to traditional interference methods. However, existing research rarely involves joint air-ground distributed communication interference, and there is a lack of the ability to schedule jammers with limited energy, a limited quantity, and different types. Based on this, this paper proposes the interference link margin in the mathematical model and normalizes and quantifies the optimization objectives. Strategies such as cycle and initial quantity selection, elastic initialization, double tabu power scheduling, and jammer conversion are designed to improve the scheduling ability of the Elastic Parallel Random Search Algorithm (EPRSA) in terms of system operation duration and interference cost. The experimental results show that EPRSA can increase the success rate of searching for interference schemes by 15.4%, reduce the number of jammers by 26.4%, enhance the type scheduling ability significantly, decrease the cost by 31%, and increase the system operation duration by 45.3%.